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Overview of Data Mining and Predictive Modelling
 
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My web page: www.imperial.ac.uk/people/n.sadawi The slides can be found here: https://github.com/nsadawi/DataMiningSlides
Views: 117504 Noureddin Sadawi
What's difference?(Big data, predictive analytics, data science, data mining, business intelligence)
 
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Download "Explore Me - Find everything nearby" App from playstore: https://play.google.com/store/apps/details?id=com.yogeshkorke.admin.exploreme This video describes the short difference between Big data analytics, predictive analytics, prescriptive analytics, descriptive analytics, business intelligence, data science, machine learning, data mining and their application with help of example store sales. Like, share and subscribe more such videos!
Views: 8259 Tech Storm
Data Analytics - Descriptive , Predictive and Prescriptive Analytics
 
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@ Members ~ This video would let you know about rising importance of Analytics where by we are covering all 4 Branches of Analytics like Financial Analytics , Risk Based Analytics , Cash Flow Analytics and Data Analytics. Video would also let you know about 3 types of Analytics covering Descriptive Analytics , Predictive Analytics and Prescriptive Analytics. You are most welcome to connect with us at 91-9899242978 (Handheld) , Skype ~ Rahul5327 , Twitter @ Rahulmagan8 , [email protected] , [email protected] or visit our website - www.treasuryconsulting.in
MicroStrategy - Data Mining & Predictive Analytics - Online Training Video by MicroRooster
 
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Source: MicroRooster.blogspot.com Format: A MicroStrategy Online Training Video blog. Description: An introduction to Data Mining & Predictive Analytics using MicroStrategy. This demo explains how to use MicroStrategy for performing advanced data science analysis. Must have some understanding of basic data mining to take advantage of this entry level demo.
Views: 16174 MicroRooster
Predictive Analytics using Orange Data Mining
 
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Data Mining Fruitful and Fun Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox. Download Link: https://orange.biolab.si/download/
Views: 797 Anurag P
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 97387 edureka!
Data Science - Part I - Building Predictive Analytics Capabilities
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This is the first video lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Views: 159604 Derek Kane
Predictive Analytics & Machine Learning with SAP HANA
 
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Predictive Analytics & Machine Learning with SAP HANA combines the depth and speed of in-memory analytics with the power of native predictive algorithms. Together with SAP Predictive Analysis for visualization, R's extensive library of statistical and data mining techniques, and the SAP HANA predictive analytic library, you get everything you need to predict the future -- in real-time.
Views: 56075 SAP Technology
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 28709 GRIETCSEPROJECTS
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 19545 Red Apple Tutorials
Predicting Football Matches Using Data With Jordan Tigani - Strata Europe 2014
 
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A keynote address from Strata + Hadoop World Europe 2014 in Barcelona, "Predictive Analytics in the Cloud: Predicting Football." Watch more from Strata Europe 2014: http://goo.gl/uqw6WS Visit the Strata website to learn more: http://strataconf.com/strataeu2014/ Subscribe for more from the conference! http://goo.gl/szEauh How can you turn raw data into predictions? How can you take advantage of both cloud scalability and state-of-the-art Open Source Software? This talk shows how we built a model that correctly predicted the outcome of 14 of 16 games in the World Cup using Google’s Cloud Platform and tools like iPython and StatsModels. I’ll also demonstrate new tools to integrate iPython with Google’s cloud and how you can use the same tools to make your own predictions. About Jordan Tigani (Google): Jordan Tigani has more than 15 years of professional software development experience, the last 4 of which have been spent building BigQuery. Prior to joining Google, Jordan worked at a number of star-crossed startups, where he learned to make data-based predictions. He is a co-author of Google BigQuery Analytics. When not analyzing soccer matches, he can often be found playing in one. Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 82911 O'Reilly
The Predictive Enterprise: Where data science meets supply chain
 
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The supply chain is one of the largest sources of big data; it carries and produces information that affects almost every other area of the business. Most businesses are not using this potential treasure-trove of information effectively, despite recognizing its value. Companies must progress from using analytics to describe the past and begin using it to inform future business. The predictive supply chain is the essential foundation of a re-imagined predictive enterprise. DHL is a collaborative and strategic partner to its customers that factors in their wider business operations to develop smarter supply chain solutions. To find out more, read the whitepaper, ‘The Predictive Enterprise: Where data science meets supply chain.’ http://www.dhl.com/predictive
Views: 18333 DHL
Making Predictions with Data and Python : Predicting Credit Card Default | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 13769 Packt Video
Enhanced Resource Allocation: Business Use of Predictive Analytics and Data Mining
 
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Visit http://tdwi.org for more information on business intelligence and data warehousing training and education. TDWI Boston 2014 Keynote: Enhanced Resource Allocation: Business Use of Predictive Analytics and Data Mining Tony Rathburn Senior Consultant & Training Director The Modeling Agency StarSoft Solutions, Inc. Advanced technology has been a cultural obsession over the past few decades as business and government have invested heavily in pursuit of competitive advantage. The exponential growth in data repositories combined with advances in analytic techniques have left many organizations searching for the opportunities that justify these investments. Predictive analytics expert and author Tony Rathburn explores a business-driven perspective on using analytics that offers measurable organizational benefits, rapid implementation potential, minimal new investments, and lowrisk implementation strategies that can have near-immediate impact on virtually all organizations.
Views: 529 TDWI
DePaul University Center for Data Mining & Predictive Analytics - Jonathan F.
 
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DePaul University graduate student Jonathan Feigenbaum discusses the importance of a degree in analytics, and shares his excitement around the new Center for Data Mining and Predictive Analytics.
Views: 1252 timjpowers
Data Mining and Predictive Analytics Graduate Program
 
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Learn more at http://www.stjohns.edu/gradpremier/ms-data-mining
Views: 2747 St. John's University
Predicting Stock Prices with SSAS Mining Models
 
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Predictive analytics and supervised machine learning with SSAS and C#. In this demo I use MS Time Series Mining structure within SSAS to predict stock prices using the Auto Regressive Integrated Moving Average (ARIMA) method. This is a bit of supervised machine learning with analysis services. I then query the mining model with SSMS and run a prediction query from a C# applications
Views: 2721 sackdeezle
What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning
 
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What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning - PREDICTIVE ANALYTICS definition - PREDICTIVE ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions." In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization. Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Views: 1040 The Audiopedia
Applications of Predictive Analytics in Legal | Litigation Analytics, Data Mining & AI | Great Lakes
 
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#PredictiveAnalytics | Learn the prediction of outcome or treatment of a case by legal courts of Appeals based on historical data using predictive analytics. Watch the video to understand analytics in legal using case study on real-life data set. How litigation analytics can flourish with the use of data mining and AI. Know more about our analytics Program: PGP- Business Analytics: https://goo.gl/V9RzVD PGP- Big Data Analytics: https://goo.gl/rRyjj4 Business Analytics Certification Program: https://goo.gl/7HPoUY #LegalTech #LegalAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 675 Great Learning
Manage the Data Deluge with Data Mining and Predictive Analytics
 
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http://www.sas.com/technologies/analytics/datamining/index.html Learn how data mining can be applied to identify trends, patterns and relationships while predictive analytics can be used to predict future outcomes. SAS® ENTERPRISE MINER™ Reveal valuable insights with powerful data mining software. Descriptive and predictive modeling provide insights that drive better decision making. Now you can streamline the data mining process to develop models quickly. Understand key relationships. And find the patterns that matter most. Benefits: * Build better models with the best tools. * Empower business users. * Improve prediction accuracy. Share reliable results. * Automate model deployment and scoring. LEARN MORE ABOUT SAS ENTERPRISE MINER http://www.sas.com/en_us/software/analytics/enterprise-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 83120 SAS Software
Constructing Predictive Model Using IBM SPSS Modeler
 
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This tutorial shows how to construct a predictive model using IBM SPSS Modeler. We use the Boston Housing dataset for our illustration. In addition, we also discuss how to evaluate the performance of the model using different nodes such as Graph Evaluation and Data Analysis Node. I hope you enjoy it and please let me know if you have any questions. Thanks for watching.
Views: 16773 IT_CHANNEL
AI for Marketing & Growth #1 - Predictive Analytics in Marketing
 
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AI for Marketing & Growth #1 - Predictive Analytics in Marketing Download our list of the world's best AI Newsletters 👉https://hubs.ly/H0dL7N60 Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind.. Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification. So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers. Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers! Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI. Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage. You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside. For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought. Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks. Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products. The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent. To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 1. The right questions 2. The right data 3. The right technology 4. The right people Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics. Other topics discussed: Ai analytics case study artificial intelligence big data deep learning demand forecasting forecasting sales machine learning predictive analytics in marketing data mining statistical modelling predict the future historical data AI Marketing machine learning marketing machine learning in marketing artificial intelligence in marketing artificial intelligence AI Machine learning ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Snapchat: growthtribe Video URL: https://youtu.be/uk82DHcU7z8
Views: 13638 Growth Tribe
Predictive Analytics Process & Tools
 
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Introduction to predictive analytics. Overview of the process, opportunities, challenges and free tools available. Link to post: http://storybydata.com/predictive-analytics-101/
Views: 6768 Story by Data
RapidMiner Tutorial - Overview of the Data Mining and Predictive Analytics
 
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A tutorial overview of RapidMiner, an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/ www.RPMSquared.com
Views: 9581 Predictive Analytics
Data Mining with SAP Predictive Analysis - Data Geek Challenge 2013
 
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This video illustrates an example of how to build an end-to-end machine learned model using SAP Predictive Analysis. Furthermore the video walks you through the aspect of training your model with respect to BIAS in your data. The effect of incorrect sampling data from a BIAS sorted dataset is demonstrated. The dataset is based on the well known IRIS that is provided with R. Let me know if you would like a copy of the dataset so that you can try this yourself. Finally the machine trained model is then applied to new data in order to perform predictions.
Views: 2293 Kurt Holst
The Data Science / Data Analytics / CRISP-DM Cycle
 
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I have data lying around. How do I start - and continue - if I want to build a predictive model based on these data? Well, it is not really like following a cooking recipes with precise steps. It is more like adjusting the steps here and there, going back and starting again with different parameters or maybe even more drastically anew with different algorithms. This video explains this general iterative process.
Views: 4579 KNIMETV
Predictive Analytics in Insurance
 
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If you have questions or comments on the contents of this video, please email us at [email protected] There has been considerable change in the relationships between customers and companies. Customers are in control of the relationships with their vendors and are not afraid to switch to a new provider if they do not feel they are receiving the service they deserve. Companies now have the ability to know their customers and market to them on a personalized basis using data mining and predictive analytics technologies. Predictive Analytics unlock insights that enable companies to add new customers and grow their existing business by improving their understanding of what their customers want. It uncovers hidden insights in customer data to create more personalized customer experiences that win more business while reducing costs and increasing customer loyalty. Predictive Analytics enable the very sharpest competitive edge. They deliver powerful, unique, qualitative differentiation by providing your enterprise a proprietary source of business intelligence with which to compete in Operations, Customer or Threat & Fraud applications in your organization. A predictive model generated from your data taps into experience to which only your company is privy, since it is unique to your prospect list and to the product and marketing message to which your customers respond (both positively and negatively). Therefore, the model's intelligence and insights are outside the reaches of common knowledge, and the top prospects it flags compose a customized, proprietary contact list. View this informative webinar to learn more about how Predictive Analytics are making a difference in the insurance industry through focused target marketing, and more efficient fraudulent claim detection. We discuss a detailed use-case for a real-world insurance company examining how specific customer attributes were used as indicators for fraud prediction.
Views: 11306 LPA Software Solutions
Predicting Stock Prices - Learn Python for Data Science #4
 
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In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 485509 Siraj Raval
Introduction to Data Mining in SQL Server Analysis Services
 
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Data mining is one of the key hidden gems inside of Analysis Services but has traditionally had a steep learning curve. In this session, you'll learn how to create a data mining model to predict who is the best customer for you and learn how to use other algorithms to spend your marketing model wisely. You'll also see how to use Time Series analysis for budget and forecast prediction. Finally, you'll learn how to integrate data mining into your application through SSIS or custom coding.
Views: 7549 PASStv
Using R and Apache Hadoop for Data Mining and Statistical Predictive Analytics
 
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This on-demand webinar, we'll: - Walk you through how Hadoop is being used today - Discuss real-world customer use cases for data mining and statistical predictive analytics in Hadoop - Show a live churn analytics demonstration with Revolution Analytics and Hortonworks Data Platform
Views: 10013 Hortonworks
Movie Success Prediction Using Data Mining Project
 
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Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 17678 Nevon Projects
Data Mining and Predictive Analytics Final Project
 
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Modeling Analysis (Education Dataset) in SPSS
Views: 70 gong wen
Machine Learning Tutorial 2 - Intro to Predictive Data Analytics
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Intro to Predictive Analytics is the second video in this machine learning course. This video explains how machine learning algorithms are used in the field of data analytics to create models of reality. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 7645 Caleb Curry
Predictive Modelling Techniques | Data Science With R Tutorial
 
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This lesson will teach you Predictive analytics and Predictive Modelling Techniques. Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE After completing this lesson you will be able to: 1. Understand regression analysis and types of regression models 2. Know and Build a simple linear regression model 3. Understand and develop a logical regression 4. Learn cluster analysis, types and methods to form clusters 5. Know more series and its components 6. Decompose seasonal time series 7. Understand different exponential smoothing methods 8. Know the advantages and disadvantages of exponential smoothing 9. Understand the concepts of white noise and correlogram 10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc 11. Understand all the analysis techniques with case studies Regression Analysis: • Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables. • It predicts the value of a dependent variable based on one or more independent variables • Coefficient explains the impact of changes in an independent variable on the dependent variable. • Widely used in prediction and forecasting Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Predictive-Analytics-0gf5iLTbiQM&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice. Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 202255 Simplilearn
Doing predictive modeling using R - Rattle (Togaware)
 
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This session covers equivalent of all SAS procedures using free software - R Rattle. Hypothesis testing, Linear and Logistic regression, Cluster Analysis. Introduction to Random Forests, SVM, Boosting etc. www.learnanalytics.in
Views: 25606 Learn Analytics
INTRODUCTION TO CLASSIFICATION - DATA MINING
 
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Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. Next the algorithm is given a data set not seen before, called prediction set, which contains the same set of attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and produces a prediction.
Views: 31668 Nina Canares
Technical Course: Decision Trees: Decision Tree Analysis
 
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Decision Tree Tutorial and Introduction by Jigsaw Academy. This is part one of the Decision Tree tutorial from our Foundation Analytics course (http://www.jigsawacademy.com/online-analytics-training). In this example, we look at how decision trees can be used by credit card companies to market themselves to a target audience of potentially profitable customers. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training. Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 80223 Jigsaw Academy
Difference between Classification and Regression - Georgia Tech - Machine Learning
 
03:29
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 66008 Udacity
DI&A Slides: Descriptive, Prescriptive, and Predictive Analytics
 
01:01:59
Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets. This webinar will compare and contrast these different data analysis activities and cover: - Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis - Descriptive Data Analytics – finding patterns - Predictive Analytics – creating models of behavior - Prescriptive Analytics – acting on insight - How the analytic environment differs for each
Views: 1976 DATAVERSITY
Data Mining Lecture - - Data Analytics life cycle (Eng-Hindi)
 
06:40
Business Intelligence Big data issues are solved using data analytics life cycle The key roles of data analytics are explained -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 17509 Well Academy
Forecasting with Predictive Analytics
 
57:56
Forecasting with predictive analytics offers the opportunity to leverage the huge amounts of data, now readily available, that exhibit the following characteristics: +Data that changes over time, as well as static data +Perhaps hundreds, thousands of independent variables +Data reflective of today’s ever-changing economic environment With data mining and machine learning methods, your data can easily and quickly be converted into knowledge to yield more accurate and more actionable models. We will demonstrate these techniques on a data set from a Brazilian grocery store chain. Information including date, store, promotions, climate, and unit price will be used to predict the total sales of oranges for the company. The model will help you identify what affects sales, decide on best prices and promos, and most importantly, forecast sales in the future. Access the software and slide deck: http://info.salford-systems.com/forecasting-webinar http://www.salford-systems.com
Views: 1375 Salford Systems
Data Mining Techniques to Prevent Credit Card Fraud
 
07:11
Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.
Views: 10666 Ben Rodick
Eight Data Science Algorithms | Data Analytics
 
10:26
In this video, you will be introduced to eight very important data science algorithms used by data scientists on daily basis Contact us : [email protected]
Views: 9467 Analytics University
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Science Training | Edureka
 
01:21:31
( Data Science Training - https://www.edureka.co/data-science ) This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples. Below are the topics covered in this tutorial: 1) Machine Learning Introduction 2) Classification 3) Types of classifiers 4) Decision tree 5) How does Decision tree work? 6) Demo in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #decisiontree #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 52594 edureka!
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
36:36
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 50005 edureka!
Introduction to Data Science with R - Data Analysis Part 1
 
01:21:50
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 858177 David Langer

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